{"id":6331,"date":"2024-07-09T12:39:10","date_gmt":"2024-07-09T10:39:10","guid":{"rendered":"https:\/\/francestat.com\/?page_id=6331"},"modified":"2024-12-02T11:46:05","modified_gmt":"2024-12-02T10:46:05","slug":"uniwin-dbscan","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-dbscan\/","title":{"rendered":"UNIWIN – DBSCAN"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Classification par la m\u00e9thode DBSCAN<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]DBSCAN (Density-Based Spatial Clustering of Applications with Noise) est un algorithme de partitionnement de donn\u00e9es propos\u00e9 en 1996 par Martin Ester, Hans-Peter Kriegel, J\u00f6rg Sander et Xiaowei Xu.<\/p>\n

Les classes form\u00e9es correspondent \u00e0 des r\u00e9gions denses dans l\u2019espace des donn\u00e9es s\u00e9par\u00e9es par des r\u00e9gions de plus faibles densit\u00e9s de points. L’algorithme DBSCAN repose sur cette notion intuitive de classes et de bruit. Il poss\u00e8de plusieurs avantages par rapport aux autres techniques de partitionnement, notamment sa capacit\u00e9 \u00e0 cr\u00e9er un nombre de classes non d\u00e9fini a priori, \u00e0 reconna\u00eetre des classes non convexes et \u00e0 isoler les points suspects.<\/p>\n

La proc\u00e9dure affiche un rapport indiquant pour chaque observation sa classe, sa distance au point central, son coefficient de silhouette ainsi qu\u2019une synth\u00e8se de la classification. Les graphiques des distances K-NN, des distances d\u2019accessibilit\u00e9 par densit\u00e9, des coefficients de silhouette et des nuages des points des classes avec ou sans enveloppes sont propos\u00e9s.<\/p>\n

Cette proc\u00e9dure est bas\u00e9e sur le package R ‘dbscan’.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6353″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][vc_column_text]<\/p>\n

Tableaux<\/u><\/span><\/strong><\/p>\n\n\n\n\n
\n

Classification des observations<\/p>\n<\/td>\n<\/tr>\n

\n

Synth\u00e8se de la classification<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/u><\/span><\/strong><\/p>\n\n\n\n\n\n\n\n
\n

Graphique des distances K-NN<\/p>\n<\/td>\n<\/tr>\n

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Graphique des distances d’accessibilit\u00e9 par densit\u00e9<\/p>\n<\/td>\n<\/tr>\n

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Graphique des coefficients de silhouette<\/p>\n<\/td>\n<\/tr>\n

\n

Nuages de points des classes form\u00e9es<\/p>\n<\/td>\n<\/tr>\n

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Nuages de points des classes form\u00e9es avec enveloppes<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:http%3A%2F%2Fwww.francestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FClassification%20par%20la%20m%25e9thode%20DBSCAN.pdf|title:UNIWIN%20-%20DBSCAN\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]DBSCAN (Density-Based Spatial Clustering of Applications with Noise) est un algorithme de partitionnement de donn\u00e9es propos\u00e9 en 1996 par Martin Ester, Hans-Peter Kriegel, J\u00f6rg Sander et Xiaowei Xu. Les classes form\u00e9es correspondent \u00e0 des r\u00e9gions denses dans l\u2019espace des donn\u00e9es s\u00e9par\u00e9es par des r\u00e9gions de plus faibles densit\u00e9s de points. L’algorithme DBSCAN repose sur cette…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-6331","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUNIWIN - DBSCAN - FRANCESTAT<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/francestat.com\/index.php\/uniwin-dbscan\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"UNIWIN - DBSCAN - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]DBSCAN (Density-Based Spatial Clustering of Applications with Noise) est un algorithme de partitionnement de donn\u00e9es propos\u00e9 en 1996 par Martin Ester, Hans-Peter Kriegel, J\u00f6rg Sander et Xiaowei Xu. 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